Student list business
A Sociological Analysis of Structural Racism in Student List Products
ozanj.github.io/student_list_policy/slides/student_list_cshpe.html
— .section
Introduction
College Board Search and student outcomes
Howell, Hurwitz, Mabel, and Smith (2021)
Scholarship on college access
Extant literatures, not mutually exclusive
- Student behavior
- Behavior of schools and postsecondary institutions (PSIs)
- Scholarship on enrollment management fits here
- Scholarship on admissions fits here
- Scholarship on enrollment management fits here
- Effects of federal, state, and local policies
Third-party providers; the other for-profit industry in education
- Schools and PSIs outsource functions to vendors and consultancies (Jaquette, Salazar, and Martin, 2022; Komljenovic, 2021; Komljenovic, 2022)
- Scholars have not investigated how third-party produces and services structure college access
Developing a literature about algorithmic products
- Literature at the intersections of access, enrollment management, and edtech
- Analyze products purchased by schools and PSIs
- Analyze the organizations that sell these products
Structural racism in algorithmic products
Structural racism
- Processes – often viewed as neutral or common-sense – that systematically advantage dominant groups and disadvantage marginalized groups
Critical turn in higher education research - Experiences of
individual people provide insight about structural racism
Critical data studies (e.g., and Benjamin, 2019; and Noble,
2018) and sociology of race (e.g., and Cottom, 2020; and Norris, 2021)
finds algorithmic reproduce/increase racial inequality -
Structurally racist inputs: Seemingly neutral inputs that
correlate with race because of historical exclusion from this input
(e.g., zip code, AP exam scores)
RQ: What is the relationship between student
list search filters (e.g., test score range, zip code) and the
characteristics of students who are included vs. excluded by student
list purchases? - Analyze student lists purchased from College Board -
Focus on race and class inequality in which prospects are
included/excluded by student list purchases
— .section
Background on student Lists
— .subsection
Lists vis-a-vis recruiting
The US market for higher education
A national voucher system - Tuition revenue: household savings; grants and loans from federal, state, and private sources - Tuition revenue follows students to Title IV institutions
Students - Goal: want to attend college - Problem: don’t know
all options, where they would be admitted, how much it will cost
Universities - Goal: enroll students to survive and other
enrollment goals - Problem: can’t rely solely on students who reach out
on their own; don’t know the prospects or how to contact them
Student lists - A matchmaking intermediary that connects institutions to prospects - “lead generation” - Student lists are an example of list-based leads, based on direct mail - As opposed to behavioral-based leads (e.g., ads from Google Search)
— &twocol
The enrollment funnel
*** =left
Prospects
- Population of desirable potential students
Leads
- Prospects whose contact info has been obtained
Inquiries
- Prospects who have contacted the institution
- Institution as first contact (leads)
- Student as first contact
Interventions along the funnel
- Convert prospects to leads
- purchase student lists
- Convert leads/inquiries to applicants
- Email, mail, targeted social media
- Convert admits to enrolles
- Financial aid packages
*** =right
Source: pngwing.com
College Board and ACT student list products
Sources of student list data
- Test takers (e.g., PSAT, SAT, AP), pre-test questionnaire (demographics, preferences)
- More recently, from college search engines (e.g., College Board Big
Future)
- Students can opt in or out
What information does a list contain (College
Board template) - Contact, demographic, college preferences, limited
academic achievement
Pricing
- Historically, a price-per-prospect model (e.g., $0.50 per name)
- ACT and College Board moving to subscription model
Buying student lists
- “Search filters” control which prospects included/excluded from a purchase
- Commonly used search filters (Link
to ACT filters)
- Graduation year, HS GPA, test score range, gender, race/ethnicity, geography (e.g., zip-code)
- New filters based on predictive analytics to facilitate
micro-targeting
- e.g., “geodemographic” filters target prospects based on past behavior of nearby students
— .subsection
EM Market dynamics
Dynamics shaping the market for student list data
From The Student List Business: Primer and market dynamics (Jaquette, Salazar, and Martin, 2022):
- Centrality of enrollment management (EM) consulting firms
- Purchase student lists on behalf of universities
- Names are input to firm predictive models and recruiting
interventions
- Competition in the 2000s
- Technology >> new sources of student list data (e.g., college search engines; software used by high schools) >> entry by new vendors (e.g., Zinch)
- Note: all student list data is derived from user-data from
students on platforms
- Concentration in the 2010s
- Horizontal acquisitions in EM consulting industry (e.g., RuffaloCODY acquires Noel-Levitz)
- Vertical acquisitions transform market for student list data (e.g.,
PowerSchool acquires Naviance/Intersect from Hobsons; EAB acquires
Cappex)
- Leverage control over pool of names to sell software-as-service
products
- Leverage control over pool of names to sell software-as-service
products
- Incumbents College Board and ACT evolve amidst threat from test-optional
- Create new search filters that aid micro-targeting of prospects
- Leverage names database to sell EM consulting
- Create/buy college search engines
— .section
Literature Review
— .subsection
Recruiting
Sociological scholarship on recruiting
Enrollment funnel: prospects, leads, inquires, applicants, admits, enrolled - Most scholarship focuses on latter stages (e.g., which applicants get admitted) - Growing body of research analyzes recruiting “in the wild”
Recruiting from perspective of high school students (Holland,
2019) - Underrepresented students sensitive to feeling “wanted” by
colleges
Connections between (fancy) high schools and (fancy) colleges
from an orgs perspective - Off-campus recruiting visits indicate a
network tie and enrollment priorities - recruiting from perspective of:
private college (Stevens, 2007); private HS counselors (Khan, 2011) -
Recruiting visits by public research universities (e.g., Salazar,
Jaquette, and Han, 2021; and Salazar, 2022)
Recruiting at open-access PSIs for adults (e.g., and Cottom,
2017; and Posecznick, 2017) - For-profits have demand in Black/Latinx
communities because traditional colleges ignore
them
Hook - Scholarship assumes that recruiting is something done by
colleges - Ignores products and consultancies that structure
recruiting
— .subsection
Sociology of race
Algorithms and actuarialism
Algorithms
- “sets of instructions written as code and run on computers” (Burrell and Fourcade, 2021, p. 215)
Algorithmic products utilize actuarial methods and logic
- Actuarial methods proceed in two steps (Hirschman and Bosk,
2020)
- Model previous cases in order to identify determinants of an outcome
- Apply these results to future cases in order to make predictions and assign risk levels
- Acturarialism (Simon, 1988)
- ideology that equates fairness with risk, as determined by predicted probabilities
- e.g., businesses that have characteristics associated with default should pay higher interest rates
Actuarialism and standardization
- Actuarial products remove individual judgment from decision-making
- Reduces racial inequity due to prejudice of individual decision-makers (Hirschman and Bosk, 2020)
Actuarial methods reproduce structural racism
Classification situations (Fourcade and Healy, 2013)
- Defined as use of actuarial techniques by orgs to categorize consumers into different groups
- Binary classifications: loans offered to consumers with “good” credit, but not bad credit
- Advances in data analytics >> categorize customers into many
groups
- Tiered products with costs and benefits tied to level of risk (e.g., payday loan)
- Predatory inclusion (Seamster and Charron-Chénier, 2017; Cottom,
2020)
- Target marginalized consumers for “democratizing mobility schemes on extractive terms”
Structurally racist inputs
- Actuarial products predict future outcome by modeling determinants
using historical data
- “Predicting the future on the basis of the past threatens to reify and reproduce existing inequalities of treatment by institutions” (Burrell and Fourcade, 2021, p. 224).
- Structurally racist input: determinant correlated with race because people of color have been historically excluded (e.g., Obermeyer, Powers, Vogeli, and Mullainathan, 2019)
- Structural racism in Moody’s credit rating algorithm for city
governments (Norris, 2021)
- %Black negatively associated with city rating until control for median household income
- Median household income is a “racialized input”:
- Seemingly neutral, structurally racist input that masks structural racism of algorithm
Micro-targeting and market segments
- Micro-targeting: identify granular segments of society with great precision
- Market segmentation: categorize customers into groups for advertisers (e.g., “married sophisticates”)
Racial exclusion is consequence of micro-targeting, market segmentation (Benjamin, 2019; Noble, 2018)
- These technologies could be used to target marginalized groups, but in practice they are not
- When developing classification systems, developer bias and structurally racist inputs enter algorithm
- Classification systems designed for profit, do not create audience segments not valued by advertisers
Micro-targeting and segmentation by Facebook (Cotter, Medeiros, Pak, and Thorson, 2021, p. 1)
- “Driven not by a goal of making all users available to advertisers, but of making the ‘right’ individuals”
- Tells advertisers choose “‘targeting strategy that focuses on reach and precision and eliminates waste’”
Student list products - College Board Student Search: “create a
real pipeline of best-fit prospects” - Ruffalo Noel Levitz (2021):
“target the right students in the right markets” by making “the most
efficient name purchases using predictive modeling”
Hook: Sociology of race has not studied products that help orgs
identify customers (I think)
— .section
Conceptual Framework
Conceptual Framework
Primary research question:
- What is the relationship between student list search filters (e.g., test score range, zip code) and the characteristics of students who are included vs. excluded in student lists purchased from College Board?
Two mechanisms of racial and socioeconomic exclusion in student list products (Salazar, Jaquette, and Han, 2022):
- Who is included in the underlying database
- Structurally racist and classist search filters
Salazar, Jaquette, and Han (2022) categorizes College Board search filters into four buckets:
- Geographic
- Academic
- Demographic
- Student preferences
Drawing largely from the sociology of race, we develop
expectations about which filters are associated with problematic
exclusion
Geographic filters
Geographic search filters enable universities to target prospects based on where they live
- e.g., state, zip code, CBSA, “geomarket,” geodemographic market segment)
Critical geography and whiteness as property Harris (1993); Salazar (2022)
- Residential segregation a function of historic and contemporary laws/policies/practicies
- Geographic filters are built on the back of racial segregation
- Targeting prospects based on location (space) without considering history of segregation (place) reinforces race-based inequality
Expected results
- Small geographic filters (e.g., zip code vs. metro) >> racial
disparities because segregation granular
- Targeting affluent communities >> racial exclusion because POC historically excluded
- Filters that create new borders based on historic education data
>> racial disparities because borders reflect historic disparities
in educational opportunities
- “Geographic education market” filters: geomarket, geodemographic segment
- These filters increases the effects of historic place-based
inequality
- Discriminate between prospects based on previously unknown geographic borders
— .section
Methods
— .subsection
Data collection
Data collection overview
Data collection
- Issued public records requests to all public universities in four states (CA, IL, MN, TX)
- Target student list vendors: College Board, ACT
- Data collection began February 2020, sought purchases from 2016-2020
For each purchased list, sought two pieces of data
- “Order summary” specifying search filter criteria (LINK)
- De-identified prospect-level student list (LINK)
Empirical research questions
- Which filter criteria were selected in student lists purchases?
- What are the characteristics of prospects included in student lists purchases?
- What is the relationship between student list filter criteria and the characteristics of purchased prospects?
Summary of data received
| State | # received order summary | # no order summary | # received list | # no list | # received both | # did not receive both |
|---|---|---|---|---|---|---|
| CA | 9 | 23 | 13 | 19 | 9 | 23 |
| IL | 9 | 3 | 9 | 3 | 8 | 4 |
| TX | 15 | 20 | 16 | 19 | 10 | 25 |
Orders and prospects purchased
— .subsection
Research design
Summary of orders and prospects
| RQ1 | RQ3 | RQ2 | RQ3 |
|---|---|---|---|
| # orders total | # orders with list | # prospects total | # prospects with order |
| 830 | 414 | 3,663,257 | 2,549,085 |
Empirical research questions
- Which filter criteria were selected in student lists purchases?
- Unit of analysis = order; 830 orders (by 14 universities)
- What are the characteristics of prospects included in student lists purchases?
- unit of analysis = university-prospect; 3,663,257 prospects (by 14 universities)
- What is relationship between filter criteria and characteristics of purchased prospects?
- Unit of analysis = order-prospect; 414 orders associated with 2,548,085 prospects
Case study research design because non-random sample
- RQ1 and RQ2
- Internal validity: are orders/prospects representative of behavior of 14 universities in sample?
- External validity: cannot make inferences about population of public univs
- RQ3
- Ixternal validity: set of search criteria yield same prospects regardless of which univ purchases
- Analyses focus on “deep dives” of conceptually important order combinations
— .section
Results
— .subsection
RQ1
— .subsubsection
Broad patterns
Filters used in order purchases
— .subsubsection
Academic filters
GPA filter used
SAT filter used
PSAT filter used
— .subsubsection
Geographic filters
State filter used by research universities, out-of-state
State filter used by research universities, in-state
— .subsubsection
Demographic filters
Race filter
— .subsubsection
Combination of filters
Filter combos used in order purchases
| Research | MA/doctoral | ||||
|---|---|---|---|---|---|
| Filters | Count | Percent | Filters | Count | Percent |
| HS grad class, GPA, SAT, PSAT, Rank, State, Race | 39 | 10% | HS grad class, GPA, SAT, Zip code | 206 | 45% |
| HS grad class, PSAT, State | 27 | 7% | HS grad class, GPA, PSAT, Zip code | 145 | 32% |
| HS grad class, GPA, PSAT, State, Race | 20 | 5% | HS grad class, SAT, State | 31 | 7% |
| HS grad class, PSAT, State, Low SES | 20 | 5% | HS grad class, GPA, SAT, PSAT, Zip code | 28 | 6% |
| HS grad class, GPA, PSAT, State | 17 | 5% | HS grad class, GPA, SAT, State | 7 | 2% |
| HS grad class, GPA, SAT, State | 16 | 4% | HS grad class, SAT, Geomarket | 6 | 1% |
| HS grad class, GPA, AP score, Geomarket | 15 | 4% | HS grad class, GPA, SAT, County | 5 | 1% |
| HS grad class, GPA, SAT, PSAT, State, Segment, Gender | 13 | 3% | HS grad class, GPA, SAT, PSAT, County | 4 | 1% |
| HS grad class, PSAT, Geomarket | 12 | 3% | HS grad class, GPA, PSAT, State | 2 | 0% |
| HS grad class, SAT, State, Low SES, College size | 11 | 3% | HS grad class, SAT, Geomarket, College type | 2 | 0% |
— .subsection
RQ2
Characteristics of Prospects
Number of prospects by university type and location
— .subsubsection
Public research universities
Racial composition of prospects in lists purchased
Median household income of prospects in lists purchased
Locale of prospects in lists purchased
— .subsubsection
Public ma/doctoral universities
Racial composition of prospects in lists purchased
Median household income of prospects purchased
Locale of prospects in lists purchased
— .subsection
RQ3
— .subsubsection
Characteristics by filters
Prospect characteristics across individual filter criteria
| Academic | Geographic | Demographic | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| All domestic | GPA | PSAT | SAT | HS rank | AP score | Zip code | State | Geomarket | Segment | CBSA | Race | Gender | ||||
| Total | 3,547,620 | 1,101,266 | 1,812,447 | 971,237 | 146,660 | 75,479 | 165,924 | 1,173,678 | 1,056,951 | 186,519 | 146,313 | 279,626 | 39,546 | |||
| Location | ||||||||||||||||
| % In-state | 38 | 62 | 30 | 54 | 83 | 42 | 98 | 48 | 17 | 15 | 4 | 59 | 6 | |||
| % Out-of-state | 62 | 38 | 70 | 46 | 17 | 58 | 2 | 52 | 83 | 85 | 96 | 41 | 94 | |||
| Race/ethnicity | ||||||||||||||||
| % White | 48 | 45 | 50 | 47 | 51 | 17 | 43 | 42 | 57 | 51 | 53 | 25 | 47 | |||
| % Asian | 16 | 15 | 17 | 15 | 10 | 7 | 13 | 18 | 13 | 27 | 28 | 5 | 38 | |||
| % Black | 5 | 7 | 4 | 7 | 8 | 17 | 8 | 5 | 4 | 3 | 2 | 11 | 1 | |||
| % Latinx | 21 | 24 | 19 | 22 | 23 | 46 | 27 | 24 | 16 | 11 | 8 | 46 | 6 | |||
| % AI/AN | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 2 | 0 | |||
| % NH/PI | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |||
| % Multiracial | 5 | 5 | 5 | 5 | 5 | 10 | 4 | 6 | 5 | 5 | 5 | 9 | 5 | |||
| % Other | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |||
| % No response | 4 | 3 | 3 | 3 | 2 | 1 | 4 | 3 | 4 | 3 | 3 | 2 | 3 | |||
| % Missing | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | |||
| Gender | ||||||||||||||||
| % Male | 34 | 19 | 37 | 18 | 0 | 3 | 46 | 24 | 48 | 6 | 0 | 11 | 0 | |||
| % Female | 36 | 23 | 40 | 20 | 1 | 15 | 54 | 27 | 52 | 9 | 0 | 12 | 33 | |||
| % Other | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |||
| % Missing | 30 | 58 | 22 | 63 | 99 | 82 | 0 | 49 | 0 | 85 | 1 | 77 | 67 | |||
| Household income | ||||||||||||||||
| Median income | $107K | $105K | $108K | $105K | $99K | $90K | $97K | $105K | $107K | $130K | $135K | $94K | $127K | |||
| Locale | ||||||||||||||||
| % City | 27 | 27 | 27 | 26 | 26 | 31 | 31 | 30 | 23 | 24 | 22 | 29 | 26 | |||
| % Suburban | 44 | 47 | 44 | 48 | 53 | 40 | 42 | 42 | 46 | 54 | 57 | 47 | 49 | |||
| % Rural - Fringe | 22 | 20 | 22 | 20 | 15 | 23 | 19 | 22 | 23 | 19 | 19 | 19 | 23 | |||
| % Rural - Distant | 6 | 6 | 5 | 6 | 6 | 5 | 7 | 6 | 6 | 2 | 1 | 6 | 2 | |||
| % Rural - Remote | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | |||
| % Missing | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |||
— .subsubsection
Zip code & test score filters
Los Angeles prospects from top income decile zip codes
— .subsubsection
Geodemographic segment filters
Filter by neighborhood segments
| 2011 D+ Cluster | SAT Math | SAT CR | Going Out of State | Percent NonWhite | Need Financial Aid | Med Income |
|---|---|---|---|---|---|---|
| 51 | 546.00 | 533.00 | 32% | 30% | 57% | $95,432 |
| 52 | 480.00 | 470.00 | 30% | 58% | 71% | $63,578 |
| 53 | 561.00 | 544.00 | 32% | 50% | 55% | $92,581 |
| 54 | 458.00 | 443.00 | 25% | 83% | 76% | $38,977 |
| 55 | 566.00 | 565.00 | 52% | 24% | 63% | $71,576 |
| 56 | 420.00 | 411.00 | 29% | 93% | 66% | $35,308 |
| 57 | 541.00 | 519.00 | 52% | 47% | 43% | $67,394 |
| 58 | 533.00 | 489.00 | 28% | 87% | 69% | $68,213 |
| 59 | 561.00 | 562.00 | 52% | 24% | 74% | $54,750 |
| 60 | 589.00 | 590.00 | 63% | 37% | 36% | $104,174 |
| 61 | 585.00 | 567.00 | 51% | 30% | 40% | $123,858 |
| 62 | 596.00 | 595.00 | 67% | 24% | 72% | $59,824 |
| 63 | 548.00 | 541.00 | 39% | 23% | 65% | $69,347 |
| 64 | 466.00 | 466.00 | 48% | 34% | 29% | $49,829 |
| 65 | 440.00 | 433.00 | 23% | 93% | 78% | $45,081 |
| 66 | 499.00 | 492.00 | 20% | 12% | 76% | $50,453 |
| 67 | 519.00 | 501.00 | 27% | 53% | 59% | $60,960 |
| 68 | 552.00 | 558.00 | 52% | 35% | 65% | $57,902 |
| 69 | 534.00 | 521.00 | 37% | 19% | 65% | $88,100 |
| 70 | 613.00 | 598.00 | 65% | 29% | 61% | $86,381 |
| 71 | 405.00 | 408.00 | 39% | 97% | 68% | $42,661 |
| 72 | 399.00 | 397.00 | 31% | 87% | 47% | $32,708 |
| 73 | 528.00 | 514.00 | 29% | 42% | 62% | $90,849 |
| 74 | 433.00 | 435.00 | 29% | 84% | 79% | $44,065 |
| 75 | 459.00 | 457.00 | 28% | 85% | 72% | $50,421 |
| 76 | 514.00 | 509.00 | 27% | 38% | 64% | $61,332 |
| 77 | 502.00 | 492.00 | 26% | 18% | 75% | $62,372 |
| 78 | 594.00 | 578.00 | 56% | 26% | 39% | $134,400 |
| 79 | 550.00 | 551.00 | 57% | 32% | 74% | $40,909 |
| 80 | 534.00 | 527.00 | 39% | 39% | 65% | $49,877 |
| 81 | 491.00 | 483.00 | 27% | 57% | 72% | $63,030 |
| 82 | 496.00 | 491.00 | 29% | 21% | 75% | $53,465 |
| 83 | 500.00 | 490.00 | 19% | 26% | 71% | $49,335 |
| Total | 512.00 | 502.00 | 32% | 43% | 65% | $70,231 |
Filter by high school segments
| 2011 D+ Cluster | SAT Math | SAT CR | Going Out of State | Percent NonWhite | Need Financial Aid | Med Income |
|---|---|---|---|---|---|---|
| 51 | 462.00 | 457.00 | 14% | 33% | 68% | $40,918 |
| 52 | 489.00 | 496.00 | 81% | 99% | 77% | $64,730 |
| 53 | 471.00 | 484.00 | 28% | 38% | 62% | $60,833 |
| 54 | 376.00 | 371.00 | 33% | 96% | 38% | $38,146 |
| 55 | 489.00 | 481.00 | 39% | 46% | 44% | $71,845 |
| 56 | 536.00 | 508.00 | 73% | 43% | 49% | $63,967 |
| 57 | 434.00 | 435.00 | 29% | 82% | 79% | $48,301 |
| 58 | 592.00 | 577.00 | 51% | 27% | 32% | $104,509 |
| 59 | 499.00 | 489.00 | 19% | 18% | 74% | $47,685 |
| 60 | 523.00 | 549.00 | 23% | 30% | 33% | $70,175 |
| 61 | 485.00 | 370.00 | 33% | 89% | 9% | $61,385 |
| 62 | 474.00 | 473.00 | 34% | 92% | 67% | $55,515 |
| 63 | 440.00 | 427.00 | 28% | 86% | 72% | $49,238 |
| 64 | 606.00 | 542.00 | 37% | 89% | 57% | $81,911 |
| 65 | 515.00 | 503.00 | 28% | 43% | 65% | $72,692 |
| 66 | 498.00 | 515.00 | 37% | 37% | 73% | $60,272 |
| 67 | 526.00 | 546.00 | 48% | 41% | 69% | $71,279 |
| 68 | 541.00 | 540.00 | 41% | 26% | 62% | $79,260 |
| 69 | 390.00 | 395.00 | 36% | 92% | 74% | $43,391 |
| 70 | 595.00 | 581.00 | 56% | 33% | 48% | $105,721 |
| 71 | 400.00 | 412.00 | 57% | 98% | 80% | $43,137 |
| 72 | 528.00 | 544.00 | 35% | 25% | 64% | $70,018 |
| 73 | 451.00 | 438.00 | 24% | 89% | 76% | $48,406 |
| 74 | 654.00 | 579.00 | 76% | 80% | 46% | $59,089 |
| 75 | 514.00 | 502.00 | 31% | 20% | 71% | $72,850 |
| 76 | 600.00 | 584.00 | 72% | 50% | 28% | $90,265 |
| 77 | 595.00 | 508.00 | 64% | 75% | 39% | $39,490 |
| 78 | 473.00 | 468.00 | 48% | 43% | 22% | $56,703 |
| 79 | 594.00 | 585.00 | 61% | 26% | 71% | $65,180 |
| Total | 514.00 | 502.00 | 32% | 44% | 65% | $70,223 |
Segment filter prospects by metro
